Jobs ML / AI

EXITEK team is looking for a Machine Learning Engineer. We're expanding our development team and looking for a passionate, experienced, self-organised candidate to work on our BeautyUp app (https://apps.apple.com/us/app/id1671843069).

Responsibilities:

• Design, develop, and optimize state-of-the-art computer vision pipelines, algorithms and models;
• Conceive and experiment with innovative features and functionalities in the realm of photo editing and enhancement;
• Collaborate with cross-functional teams to integrate machine learning models into production systems and pipelines.

Requirements:

• Proficiency in Python and PyTorch;
• Experience in Computer Vision (GANs, SD, etc.) is a must;
• Deep understanding of the current state of the art in machine learning and computer vision;
• Robust communication skills, complemented by the ability to thrive in a collaborative team environment.

We are looking for Data Scientist to join a Data Team working on innovative AI-driven products. 
 

These roles focus on building AI-powered solutions that solve real business challenges and deliver personalized customer experiences. The team leverages Generative and predictive AI to enhance customer journeys and automate operational processes.

Responsibilities:

• Collaborate closely with Product Owners, Data Analysts, Data Engineers, and ML Engineers to design, develop, deploy, and monitor scalable AI and Generative AI solutions
• Drive the adoption of Generative AI across the organization by building solutions using LLMs, embeddings, and RAG architectures
• Work directly with business stakeholders to translate requirements into production-ready AI systems
• Monitor and optimize model and system performance, applying best practices in Data Science, MLOps, and LLMOps
• Continuously improve prompts, models, and pipelines to maximize business impact
• Stay up to date with advancements in LLMs, RAG frameworks, and GenAI technologies to drive innovation

Requirements:
Must-have:

• 5 years of experience in Data Science or Applied Machine Learning
• Strong proficiency in Python and SQL
• Experience building and deploying machine learning models
• Hands-on experience with Generative AI, including:

• Embeddings
• Vector search
• RAG-style architectures
• NLP or transformer-based models
  • Understanding of advanced model evaluation methods (ranking, relevance, retrieval quality, etc.)

Nice to have:

• Experience with Snowflake
• Knowledge of AWS cloud services
• Experience with LLMOps (prompt and version management, monitoring, evaluation frameworks)

About the role
We’re hiring a Senior AI Engineer to design, develop, and deploy enterprise AI solutions including Agentic AI, intelligent automation, and predictive analytics, aligned with governance/security frameworks and business objectives.

What you’ll do

• Build AI/ML models (classification, prediction, NLP, computer vision) and optimize for performance/cost 
• Build LLM-based solutions (chatbots, copilots, automation agents) 
• Design and implement multi-agent architectures, orchestration logic, decision flows, and human-in-the-loop controls 
• Own data prep, feature engineering pipelines, training/fine-tuning/evaluation, and model lifecycle/versioning 
• Integrate AI into enterprise systems via APIs/microservices, including real-time inference services 
• Implement responsible AI practices (bias control, explainability, auditability) and secure AI pipelines/APIs (auth/authz) 
• Monitor production performance, logging/alerting, drift, and optimize latency/scalability 

Must-have

• 5+ years AI/ML engineering; production deployments; enterprise-scale AI systems/APIs 
• Hands-on LLMs/NLP or Computer Vision 
• Strong Python ML stack (TensorFlow/PyTorch/Scikit-learn) 
• LLM ecosystem experience (Azure OpenAI / OpenAI / Gemini / HuggingFace), embeddings/vector DBs 
• Agentic/orchestration frameworks (LangChain / Semantic Kernel / AutoGen; multi-agent frameworks; tool use) 
• Azure AI Services/Azure ML/Databricks + Docker/Kubernetes; REST/microservices, event-driven, APIM 

Authority / constraints

• Owns AI architecture/model development standards and Agentic AI implementation approaches 
• Cannot deploy without security/governance approval; cannot use external AI services without compliance validation 

Nice-to-have certs

• AI-102 preferred; DP-100 recommended

Хелоу, люба спільното \0/

Ми продовжуємо нарощувати команду американського Survey SaaS, що аналізує поведінку юзерів під час digital опитувань. По суті, це платформа, де бізнес може засетапити своє опитування та отримати швидкий результат, очищений від ботів + отримати advanced behavioural analytics від нашої моделі.

Вам сподобається, якщо ви:
- самодостатні та организовані.
- хочете автономію та відповідальність за свою ділянку.
- вам дійсно цікаво те, чим ви займаєтесь.

На цьому я дозволю перемкнутись на англійську, бо далі конкретніше.

About the role

Build and own ML models that predict survey respondent certainty and detect fraud from behavioral signals — mouse movement, click patterns, timing, browser/OS data. You'll be the sole ML engineer maintaining a production hybrid CNN+tabular certainty model (already in client deliverables) and building the new bot detection model. You'll work directly with a US-based AI scientist who provides strategic direction.
 

What you'll work on

• Improve the hybrid 1D CNN (mouse trajectories) + MLP (tabular features) certainty model — error analysis, hyperparameter tuning, augmentation, loss experiments 
• Build the bot detection classifier — binary human/bot model reusing behavioral pipeline infrastructure 
• Run segment-level evaluations — per-survey quality checks, precision/recall optimization, calibration analysis 
• Track experiments in MLflow — every run logged with parameters, metrics, dataset versions 
• Process raw time-series — mouse trajectories as [T, 7] tensors (x, y, speed, acceleration, dt, dx, dy) 
• Document to audit standard — code, dataframes, notebooks, reproducible results with artifact links
   

What we're looking for

• Strong PyTorch — built and trained CNNs from scratch, not just fine-tuned 
• Tabular ML experience — LightGBM, XGBoost, or gradient boosted trees 
• Rigorous evaluation mindset — calibration, segment analysis, label quality, beyond single-metric optimization 
• Time-series/sequence data experience — temporal patterns in any domain (NLP, audio, sensors, clickstream) 
• Imbalanced classification expertise — precision/recall tradeoffs, threshold tuning, AUC-ROC 
• MLflow or similar (W&B, Neptune) 
• Self-directed — you're the only ML person on execution, strategic guidance from US AI scientist 
• Auditable work — advisor reviews regularly, notebooks/scripts must be clean
• Clean verbal English
   

Nice to have

Behavioral/clickstream analysis • time-series augmentation • fraud/anomaly detection • model serving • survey methodology/psychometrics
 

Stack

Python • PyTorch • LightGBM • MLflow • HDF5 • PostgreSQL
 

What this is NOT LLM/GenAI role, but classical ML + deep learning on structured behavioral data 

The Team

• Product Manager
• 2x Fullstack Devs
• Anti-Fraud Automation QA
• DevOps
   

We offer

• 15 paid day-offs per year.
• 10 US/UA holidays.
• Flexible schedule and fully remote work from anywhere with reliable overlap to CET.
• Senior team members and strong AI Scientist Advisor.
• Minimal bureaucracy, high authonomy and trust, fast decisions.

Responsibilities

• Refactor a single-agent prototype into a multi-agent architecture (orchestrator + specialized agents).
• Design orchestration logic, agent communication, and state/session management.
• Implement guardrails (prompt-injection protection, output validation).
• Integrate external APIs with resilient error handling.
• Optimize performance and latency (<2–4 seconds).
• Implement monitoring, telemetry, and observability for production systems.
• Collaborate with cross-functional teams to ensure robust API design and integration.

Job requirements

• Strong experience with LLM systems and multi-agent architectures.
• Proficient in Python, including FastAPI, async programming, and Pydantic.
• Experience with function calling and JSON schema outputs.
• Strong API design skills and integration experience.
• Experience in Azure environment: AI Foundry, Key Vault, Managed Identity, monitoring.
• Solid experience in system architecture and production deployment.
• English: conversational; German: A2 comprehension.

Nice to Have:

• MCP certification or relevant Azure certifications.

This is not a coding role. This is a systems-of-systems role. 

If you build things with AI, this posting isn't for you. 

If you build the systems that govern how AI builds things — keep reading.

What This Role Is

LifeChef is a HIPAA-compliant healthcare platform delivering Medically Tailored Meals. Our engineering team builds on a methodology called Enso — a spec-driven, AI-augmented development framework where every feature starts with a specification, gets built with AI assistance, and is validated against defined criteria.

Enso v1 is linear: spec → generate → validate → ship. It works. But it's hitting scaling limits — the document graph grows with every feature, validation complexity compounds, and the process requires increasing human oversight to maintain quality. This is a familiar problem in any AI-assisted workflow that outgrows its initial architecture.

We need someone to take Enso from v1 to v2: a true agentic system where specialized AI agents handle research, specification validation, code generation oversight, quality assurance, and deviation detection — with structured autonomy, clear escalation paths, and human-in-the-loop at defined decision points, not everywhere.

You would partner directly with the CTO (who designed the current methodology and has strong opinions about where it needs to go) to architect, build, and maintain this system. The first 30 days are heavy collaboration — translating vision into working architecture. After that, you own the evolution of the process while the CTO focuses on product and team.

What You'd Actually Build and Maintain

• Agent topology for the development lifecycle: research agents, spec validation agents, code review agents, compliance checking agents, red-team/adversarial agents — each with defined boundaries, confidence thresholds, and escalation triggers
• Spec-to-code reconciliation system: agents that validate whether implemented code actually satisfies the specification, flagging deviations and ambiguities rather than silently passing
• Multi-model orchestration strategy: routing different tasks to different models based on cost, capability, and risk (expensive models for reasoning, cheap models for classification, open-source where it fits)
• Structured output validation: ensuring agent outputs are parseable, auditable, and actionable — not just "LLM said it's fine"
• Governance and escalation framework: what agents decide autonomously vs. what requires human sign-off, circuit breakers for low-confidence situations, audit logging for compliance
• Evolution engine: the system should improve itself — tracking where agents fail, where human overrides happen, and feeding that back into prompt refinement and architecture changes
• Integration with existing CI/CD: the agentic layer hooks into GitHub Actions, automated testing (Vitest), PHI scanning, secret detection, and compliance validation already in the pipeline

This is infrastructure that the development team operates within. You're not shipping product features — you're shipping the machine that makes product development faster, safer, and more consistent.

What We Need From You

Hard requirements:

• You have designed or built at least one agentic system where AI agents operate with structured autonomy — multi-step workflows with validation, error handling, and escalation. Not just API wrappers. Not just chatbots.
• You understand agent topology as a design problem: which agents need to exist, what their boundaries are, how they communicate, how you prevent cascading failures.
• You have hands-on experience with LLM orchestration frameworks (LangChain, CrewAI, AutoGen, custom implementations — the specific tool matters less than the depth of understanding).
• You think in terms of eval frameworks and validation: how do you know if an agent's output is good? How do you measure drift? How do you build confidence scoring that's actually useful?
• You understand multi-model strategies: when to use which model, cost/capability tradeoffs, how to tier your model usage so you're not burning expensive tokens on classification tasks.
• You can work in both Python and TypeScript, or at minimum are comfortable in one and willing to operate in the other. Our product stack is TypeScript-first but agentic tooling often lives in Python.
• Strong written communication. This role involves heavy async documentation of architectural decisions, process design, and system behavior.

What sets you apart:

• Experience with Model Context Protocol (MCP) — building custom MCP servers, tool-use architectures
• Claude Code, Cursor, or similar AI-native development tools as part of your own workflow (you should be using what you're building systems around)
• Experience in healthcare, fintech, or regulated environments where AI outputs must be auditable and compliant
• Understanding of prompt engineering at the systems level — not "write a good prompt" but "design a prompt architecture that's maintainable, versionable, and testable"
• You've thought seriously about adversarial validation — red-team agents, cross-model verification, structured disagreement as a quality signal
• You have opinions about where agentic AI is overhyped and where it genuinely works, and you can defend those opinions with specifics

What we explicitly don't require:

• A portfolio of 50 agentic projects. The field is young. One deep project with real orchestration depth beats ten shallow ones.
• Product development skills. You won't be building UI or shipping user-facing features.
• Healthcare domain expertise. We'll teach you the domain. You teach us the orchestration.

How We Evaluate

1. Screening questions (in your application) — We're looking for specificity and honesty, not keyword density.
2. Architecture conversation with CTO (90 min) — We'll describe our current system, its limitations, and where we want to go. You'll tell us how you'd approach it. This is a two-way design conversation, not a quiz.
3. Small design exercise (async, take-home) — Given a simplified version of our spec validation problem, design an agent topology. We're evaluating how you think about boundaries, failure modes, and escalation — not production-ready code.
4. Trial sprint (60–90 days) — Heavy pairing with CTO in the first 30 days, increasing autonomy after. Defined milestones at 30, 60, and 90 days.

The Team and Context

Small, senior engineering team, primarily Ukraine-based. The CTO (Jay) designed the Enso methodology, has 20+ years of engineering experience across healthcare, threat intelligence, and enterprise platforms, and has built the governance architecture (constitutions, spec-gates, validation layers) that you'd be evolving. He's technical, direct, and has strong opinions — but he's hiring you because he needs a partner in this, not an executor.

The product stack is Next.js 16, React 19, TypeScript, PostgreSQL, AWS serverless (Lambda, CDK, Amplify Gen 2), with Vitest testing, Pino structured logging, and HIPAA compliance enforced in CI. You don't need to know this stack deeply, but you need to understand it well enough to build agentic tooling that integrates with it.

We're building toward autonomous agentic systems for clinical operations — this development process work is the foundation that makes that possible. If the idea of designing the system that governs how an engineering team builds software excites you, this is the role.

How to Apply

Reply with:

• The most complex agentic or LLM orchestration system you've built — what it did, how it was structured, what you learned
• Your honest take on where agentic AI is right now and what's actually hard
• What interests you about building process infrastructure vs. product features

Generic responses get skipped. We know the field is young — we'd rather hear about one deep experience than a list of tools you've touched.

Our Customer:

Our client is a technology-focused company building high-performance, real-time ML inference systems. The team develops ultra-low-latency engines that process billions of requests per day, integrating ML models with business-critical decision-making pipelines. They are looking for an experienced backend engineer to own and scale production-grade ML services with strong focus on latency, reliability, and observability.

Your tasks:

• Lead the design and development of low-latency ML inference services handling massive request volumes.
• Build and scale real-time decision-making engines, integrating ML models with business logic under strict SLAs.
• Collaborate closely with data scientists to deploy ML models seamlessly and reliably in production.
• Design systems for model versioning, shadowing, and A/B testing at runtime.
• Ensure high availability, scalability, and observability of production systems.
• Continuously optimize latency, throughput, and cost-efficiency using modern tools and techniques.
• Work independently while collaborating with cross-functional teams including Algo, Infrastructure, Product, Engineering, and Business stakeholders.

Required Experience and Skills:

• B.Sc. or M.Sc. in Computer Science, Software Engineering, or related technical field.
• 5+ years of experience building high-performance backend or ML inference systems.
• Expert in Python and experience with low-latency APIs and real-time serving frameworks (e.g., FastAPI, Triton Inference Server, TorchServe, BentoML).
• Experience with scalable service architectures, message queues (Kafka, Pub/Sub), and asynchronous processing.
• Strong understanding of model deployment, online/offline feature parity, and real-time monitoring.
• Experience with cloud environments (AWS, GCP, OCI) and container orchestration (Kubernetes).
• Familiarity with in-memory and NoSQL databases (Aerospike, Redis, Bigtable) for ultra-fast data access.
• Experience with observability stacks (Prometheus, Grafana, OpenTelemetry) and alerting/diagnostics best practices.
• Strong ownership mindset and ability to deliver solutions end-to-end.
• Passion for performance, clean architecture, and impactful systems.

Would be a plus:

• Prior experience leading high-throughput, low-latency ML systems in production.
• Knowledge of real-time feature pipelines and streaming data platforms.
• Familiarity with advanced monitoring and profiling techniques for ML services.

As an ML Engineer, you'll work at the intersection of machine learning systems, knowledge representation, and reasoning infrastructure — building the foundational intelligence layer that powers our platform. This isn't about wrapping APIs or fine-tuning models in isolation. You'll be solving hard problems at the frontier of applied ML.

What you'll work on:

Designing ML pipelines that transform unstructured, proprietary information into semantically rich, queryable representations

Building and integrating models that enable probabilistic reasoning, multi-hop inference, and context-aware decision support

Developing agentic workflows where AI assists human judgment through explainable outputs, uncertainty quantification, and iterative feedback loops

Defining evaluation metrics and testing frameworks for high-stakes, information-led environments

Contributing to architectural decisions around ML infrastructure and model serving

Collaborating closely with backend engineers, product, and domain experts

What We're Looking For

Strong foundations in machine learning — NLP, information extraction, representation learning

Systems thinking mindset, not just model performance in isolation

Comfortable working in ambiguity and defining problems from first principles

Intellectually honest — willing to challenge assumptions, including our own

Pragmatic: ships working systems, iterates on real feedback, avoids over-engineering

Cares about reproducibility, clarity, and long-term maintainability

What We Offer

Work on genuinely hard, foundational problems at the intersection of ML and knowledge systems

Founder-led, deeply technical environment — low-ego, high-ownership

Fast feedback loops, strong opinions, high bar for clarity

Flexible/hybrid setup centred around London

Early-stage equity

Our Values

First-principles thinking. Human judgment matters. Intellectual honesty. Trust by default. Compounding advantage. Build infrastructure, not wrappers.

PwC is a global network of more than 370,000 professionals in 149 countries that turns challenges into opportunities. We create innovative solutions in audit, consulting, tax and technology, combining knowledge from all over the world.

PwC SDC Lviv, opened in 2018, is part of this global space. It is a place where technology is combined with team spirit, and ambitious ideas find their embodiment in real projects for Central and Eastern Europe.

What do we guarantee?

• Work format: Remote or in a comfortable office in Lviv - you choose.
• Development: Personal development plan, mentoring, English and Polish language courses.
• Stability: Official employment from day one, annual review of salary and career prospects.
• Corporate culture: Events that unite the team and a space where everyone can be themselves.

Join our dynamic AI team and be at the forefront of innovation, working on transformative projects for some of the most recognized companies worldwide. As an AI Consultant, you will participate in the implementation and development of AI-powered solutions across a broad range of applications, from Gen AI tools to data analytics fraud detection systems.

Key Responsibilities:

• Participate in AI projects from inception to completion, ensuring timely delivery of high-quality solutions;
• Solution Development: Design and implement cutting-edge AI and GenAI solutions, including Retrieval-Augmented Generation (RAG) for knowledge retrieval, prompt optimization, and fine-tuning of large language models (LLMs);
• Apply AI techniques to areas such as fraud detection, satellite imagery analysis, time-series forecasting, and automated data quality validation to enhance decision-making and automation;
• Develop web applications (FE / BE / cloud architecture) enabling users to interact with AI systems;
• Client Interaction: Work closely with clients to understand their unique needs and provide expert guidance and support throughout the project lifecycle.

Requirements and skills:

• 2+ years of professional experience in this or similar area;
• Experience working on AI/ML projects, in areas such as Generative AI, NLP, Computer Vision or Machine Learning model development and deployment;
• University degree in Mathematics or a similar field is an advantage;
• Programming skills in Python (pandas, plotly, numpy);
• Experience with Full stack development (JavaScript) is a significant plus.
• Experience with SQL databases; familiarity with NoSQL is a plus;
• Experience with generative AI models is a plus;
• Fluent in English (level B2 or higher).

Policy statements:
https://www.pwc.com/ua/uk/about/privacy.html

We are looking for AI-driven Software Engineer to join our long-term project.

Responsibilities:

• Design practical, scalable software architectures
• Translate business requirements into clear technical solutions
• Define data models, APIs, integrations, and system structures
• Contribute across the full lifecycle — from idea to production

You’ll be part of a small, fast-moving team where ownership and initiative are highly valued.
 

Who you are

• You take ownership and enjoy responsibility
• You communicate clearly with both technical and non-technical stakeholders
• You’re comfortable in a fast-paced, evolving environment

You also:

• Think in systems and trade-offs, not rigid rules
• Take a hands-on, proactive approach
• Balance structure with pragmatism
• Stay curious and keep up with new technologies
• See AI as a practical tool, not just a trend
   

Requirements:

You don’t need to master everything, but you’re familiar with or interested in:

• APIs and system integrations
• Modern architectures (React, React Native, TypeScript, Next.js, Supabase)
• AI-assisted development tools (e.g., Cursor, Claude Code)
• Automation tools (e.g., n8n)
• No-/low-code platforms (e.g., Bubble.io — a plus)
  
  Location Belgium must have!
   

👉 What matters most: understanding core principles, not just tools.

Trinetix is looking for a skilled AI Engineer. 

Join our team at Trinetix and collaborate on a project with a global telecommunications leader operating one of the world’s most reliable Internet backbones across more than 100 countries. In this role, you will contribute to large-scale AI initiatives focused on improving network operations through intelligent automation, predictive insights, and AI-driven workflows. 

You will work on GenAI applications, AIOps intelligence, and RAG-based systems, helping to transform how complex telecom infrastructure is monitored, analyzed, and operated. The role combines engineering, data, and product thinking to deliver AI solutions with measurable business impact. 

Join us and grow your career in a dynamic international environment while helping shape the future of AI-driven network operations. 

What You’ll Do:

• Design and implement AI/ML pipelines, including LLM-based applications, GenAI frameworks, and vector retrieval systems. 
• Build production-grade AI integrations in Python, ensuring scalability and reliability. 
• Apply enterprise AI patterns such as RAG, observability, agent workflows, and model evaluation. 
• Architect secure, scalable integrations between cloud platforms and enterprise systems. 
• Develop data pipelines, event streams, and observability frameworks to support AI at scale. 
• Collaborate with Data Scientists on feature engineering, model evaluation, and experiment design. 
• Contribute to AI-driven telecom solutions by learning and applying concepts in IP routing, EVPN/L3VPN, telemetry, and optical networking. 
• Translate technical capabilities into measurable business impact through KPIs, ROI analysis, and product-focused thinking. 

About You:

• 5+ years of experience in data, analytics, machine learning, or AI-driven environments. 
• Strong hands-on expertise with LLMs, GenAI frameworks, ML/analytics pipelines, and vector databases. 
• Proficiency in Python and experience delivering production-grade AI solutions. 
• Solid understanding of enterprise AI patterns (RAG, observability, agent workflows, model evaluation). 
• Knowledge of data pipelines, event-driven architectures, and system-to-system integration. 
• Familiarity with data science practices: feature engineering, statistical modeling, experiment design, and hypothesis-driven analysis. 
• Ability to work with structured and unstructured data, conduct exploratory analysis, and guide AI solution design. 
• Experience collaborating across engineering, architecture, IT, and operations teams. 
• Strong communication skills to simplify AI concepts for non-experts. 
• Curiosity and adaptability to learn emerging AI tools and telecom domain concepts. 

Must-haves:

• AI/ML Engineering: LLMs, GenAI frameworks, vector retrieval systems, ML lifecycle practices. 
• Programming: Python (production-grade AI integrations). 
• Architecture & Integration: Cloud-native integrations, event streams, observability, secure connectivity patterns. 
• Data Science Collaboration: Feature engineering, model evaluation, experiment design. 
• Telecom Learning Ability: Motivation to learn IP routing, EVPN/L3VPN, telemetry, optical networking. 
• Product Thinking: KPI design, ROI measurement, business impact translation. 

Ideally, You Also Have:

• Experience with RAG-based systems and advanced observability frameworks. 
• Familiarity with MLOps practices (model monitoring, validation, lifecycle management). 
• Knowledge of agent workflows and orchestration patterns in enterprise AI. 
• Exposure to telecom-specific AI use cases (network telemetry, predictive maintenance, intelligent automation). 
• Сloud-native AI platforms and scalable data infrastructure. 

What we offer:

• Continuous learning and career growth opportunities 
• Professional training and English/Spanish language classes   
• Comprehensive medical insurance 
• Mental health support 
• Specialized benefits program with compensation for fitness activities, hobbies, pet care, and more 
• Flexible working hours 
• Inclusive and supportive culture 

Domain: SaaS
Project: AI strategy product
Client: Germany
Location: Remote (attending events once a quarter across the EU)
Time Zone: CET
Contract: B2B
Budget: $6500 gross

About US

DevKit is more than just an IT company - we are a team of passionate professionals dedicated to creating cutting-edge solutions. Our employees work on exciting international startups, collaborating with highly skilled teams from around the world. With expertise in software development, design, and engineering, we help bring innovative ideas to life and drive success on a global scale!
Our mission is to connect exciting projects with top-tier professionals. We are committed to creating a dynamic space where innovation thrives, top talent excels, and global teams unite to develop cutting-edge solutions.
 

About PROJECT

The company is a trailblazing AI-powered SaaS platform, reshaping the landscape of procurement from a tactical function to a strategic business ally that orchestrates seamless value chains. The state-of-the-art solution empowers organizations to independently develop procurement strategies with a flexible framework adaptable to their specific needs and contexts.

Redefining traditional category management, the company’s all-encompassing solution takes it to new heights by harnessing the capabilities of AI, fostering extensive collaboration, and integrating data from diverse sources beyond conventional category taxonomy. At the product, they are driven by a purpose-centered ethos, dedicated to tackling ESG-related challenges, positioning procurement as a catalyst for positive change rather than merely a cost-cutting entity.

The multitude of prestigious awards and recognitions speaks to their leadership in the AI-driven procurement technology landscape, showcasing our unwavering commitment to innovation and excellence.

Main Tech Stack

AI/DS/ML related stack
 

Responsibilities
 

• AI/DS/ML initiatives organization-wide, overseeing the entire ML lifecycle from conception to execution.
• Drive continuous development of AI/ML functionalities for our SaaS product, prioritizing well-structured, secure, and scalable code, complemented by comprehensive documentation and rigorous testing protocols.
• Initiate the integration of pertinent research findings into our product, particularly in procurement, through experimentation and the creation of solutions like chatbots, forecasts, and decision support systems.
• Utilize your software engineering expertise and business acumen to foster the growth and maintenance of our ML services, guiding all stages of technology development.
• Establish and manage high-quality datasets, alongside developing data pipelines for procurement data processing.
• Collaborate closely with cross-functional teams of software engineers to enact transformative changes within procurement practices.
• Stay informed about the latest advancements in AI/ML technologies and industry best practices, actively contributing insights for continuous improvement.
• Cultivate a culture of learning, knowledge sharing, and team development within our Data Science team.

Requirements
 

• Have 5+ years of proven industry experience in applying Machine Learning and AI techniques.
• Demonstrate proficiency in Machine Learning principles and generative models, with an understanding of computational limitations and data privacy considerations.
• Showcase expertise in NLP, LLM (3+ years), including prompt engineering, training, fine-tuning, RAG, evaluation, transfer learning techniques, and anomaly detection.
• Possess knowledge of Data Analysis/Processing, Data Visualization, and Storage systems (e.g., Databases, Data Lakes, Data Warehouses).
• Advocate for best coding practices, thorough documentation, rigorous reviews, data integrity, security, and performance optimization, with expertise in the Python ecosystem and popular ML libraries/frameworks, including those tailored for LLM.
• Have experience in Infrastructure management (MLOps/LLMOps, AWS/GCP Clouds, SQL, REST APIs, GitHub), and overseeing end-to-end ML pipelines.
• Demonstrate proficient English communication skills, both verbal and written, with a focus on clarity, openness, and professionalism.

Would be a plus:
 

• Seize the opportunity to join the team and make meaningful contributions to our engineering excellence within an international team and context.
• Occasional business travel within Europe may be required, averaging once per quarter, alongside team-building company events.
• Join a pioneering AI-driven procurement technology startup, contributing to the creation of innovative solutions that significantly impact enterprise customers.

Work conditions

• A long-term role in a stable, fast-growing company
• Flexible working hours with a results-oriented approach
• Competitive salary and benefits package (vacation, sick leave, national holidays)
• Comfortable workspace, corporate events, and team-building
• Continuous learning, professional growth, and skill development

If you're looking for a place to grow, innovate, and make an impact, we'd love to have you on board!

Your Role

You will join our new AI division and take part in the full development cycle of AI products — from hypothesis validation to production-ready solutions. The role covers cross-departmental projects.

Key directions:

• RAG systems for content generation from internal knowledge bases
• AI agents for data analysis, sales support, and customer service
• End-to-end marketing analytics & automation (n8n)
• Call transcription, analysis, and AI-coaching for managers
• Data parsing and analysis from external sources with LLMs
• Predictive analytics (churn, NPS)
• Automatic generation of commercial proposals
• NLI, reranking, and other NLP tasks

Requirements

Python & Backend (3+ years of commercial experience)

• Python, FastAPI, REST API
• PostgreSQL, Redis
• Git, CI/CD, Docker

LLM & RAG

• RAG pipelines: chunking, embeddings, retrieval, reranking
• Vector DBs: Weaviate, Qdrant, Pinecone, pgvector or similar
• LLM API integration: OpenAI, Anthropic, Google Gemini
• LangChain, LlamaIndex or similar orchestration frameworks
• Prompt engineering and prompt optimization
• AI agents and agentic frameworks

Nice to Have

Backend & Infrastructure

• SSE/WebSocket, Celery, message brokers (RabbitMQ, Kafka)
• Kubernetes, AWS, GCP

LLM in Production

• Structured Outputs, Function Calling, JSON mode
• Self-hosted LLM deployment (Ollama, vLLM or similar)
• LLM Observability: LangFuse, LangSmith or similar
• Evaluation: RAG & LLM output quality (RAGAS, DeepEval or similar)
• Guardrails, PII handling, token budgeting

Data & Automation

• NLI: classification, sentiment analysis, contradiction detection
• Speech-to-Text (Whisper, Deepgram, AssemblyAI or similar)
• Document parsing and external source scraping
• Big data pipelines
• n8n or similar automation platforms

Soft Skills

• Autonomy — ability to make architectural decisions and work independently
• Systems thinking — ability to see the product holistically at the architecture level
• Speed of iteration — readiness to quickly test hypotheses and implement changes
• Communication — ability to present technical decisions to non-technical business stakeholders
• English — for technical documentation and professional growth

Hiring Process

1. General interview
2. Technical interview
3. Offer

📩  Send your CV with a description of your relevant experience.

We’re on the hunt for a talented AI/ML Engineer to design, implement, and optimize cutting-edge AI/ML solutions. If you thrive on solving complex problems and creating production-ready models, we’d love to hear from you!

.  What You’ll Do:

• Develop and fine-tune AI/ML models for performance and scalability.
• Enhance production models, debug issues, and ensure reliability.
• Work with cross-functional teams to shape future AI/ML innovations.
• Explore emerging AI/ML technologies and recommend tools/frameworks.
• Build robust pipelines using Python, SQL, Docker, and more.

.  What We’re Looking For:

• Strong experience in AI/ML, data science, or data engineering.
• Expertise in Python, TensorFlow, Keras, Scikit-learn, and Generative AI.
• Familiarity with Docker, Streamlit, and data tools like Pandas and SQL.
• Strong communication skills and the ability to work independently.

• English: Upper-intermediate or higher.

   

  What We Offer:

✔️ 18 vacation days + 12 sick leaves annually.
✔️ A high-tech workplace (MacBook Pro + Dell monitor).
✔️ Exciting projects with cutting-edge technologies.
✔️ Flexible work hours and competitive salary.
 

Ready to shape the future of AI/ML with us? 🚀 Apply now and be part of something extraordinary!

Role Summary

We are looking for a Senior ML Engineer who will be responsible for the end-to-end lifecycle of machine learning models, including design, training, evaluation, deployment, and ongoing optimization. In this role, the engineer will work on building and maintaining model ensembles, defining inference logic, establishing retraining workflows, monitoring model performance and drift, and integrating ML solutions into production systems that support real-time decision processes.

The platform follows a hybrid architecture where deterministic rule-based logic is applied first, and more complex scenarios are routed to a machine learning layer for deeper analysis.
 

Key Responsibilities:

• Design, train, fine-tune, and optimize machine learning models used for risk estimation, classification tasks, and automated decision support.
• Develop and maintain ensemble strategies that combine outputs from multiple models, including confidence-based routing and prediction aggregation.
• Build and manage retraining pipelines leveraging synthetic, curated, and eventually production-scale datasets to continuously improve model performance.
• Define evaluation methodologies, performance thresholds, fallback strategies, and human-in-the-loop review triggers for low-confidence predictions.
• Collaborate with backend and product teams to integrate model inference into real-time services and scalable production APIs.
• Improve model reliability, interpretability, and operational performance across early-stage and production workflows.
• Implement monitoring systems to track model drift, prediction quality, system latency, and conditions that trigger retraining.
• Explore and evaluate alternative domain-adapted foundation models when appropriate.
• Ensure traceability and auditability of model predictions while adhering to security, privacy, and deployment standards.
• Contribute to system architecture, MLOps practices, and deployment standards for machine learning solutions in secure cloud or on-premise environments.
   

What We’re Looking For:

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or a related technical field.
• 5+ years of practical experience building and deploying machine learning solutions in production environments.
• Strong proficiency in Python and experience with modern machine learning frameworks such as PyTorch, TensorFlow, or similar tools.
• Solid understanding of core ML concepts including supervised learning, classification, ranking methods, model calibration, and evaluation techniques.
• Experience adapting and fine-tuning domain-oriented models, as well as working with multi-model or ensemble-based approaches.
• Hands-on experience developing ML pipelines covering training, validation, deployment, and iterative retraining.
• Familiarity with integrating ML systems into API-driven services, as well as working with containers, CI/CD pipelines, and model serving infrastructure.
• Experience with experiment tracking, model version control, and monitoring model performance in production.
• Ability to work effectively with incomplete, evolving, or partially labeled datasets in real-world environments.
   

Nice to Have:

• Experience working with AI systems in regulated or compliance-sensitive environments.
• Familiarity with multilingual machine learning use cases or models designed to support multiple languages.
• Experience applying model explainability techniques, confidence estimation, and human-in-the-loop review workflows.
• Exposure to vector search, semantic retrieval methods, or document understanding pipelines.
• Experience building or operating ML solutions in privacy-sensitive environments with strict data protection requirements.
  
  We offer:
• Flexible working schedule
• Fully remote work
• Compensation for medical, educational and sports activities
• Professional growth opportunities
• Internal knowledge-sharing talks
• Awesome team events and activities
• Paid vacation and sick leave